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        "<a href=\"https://colab.research.google.com/github/tancik/fourier-feature-networks/blob/master/Experiments/1d_ntk_opt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Z77wztQoiipL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q neural_tangents==0.2.2 livelossplot"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wcn1yDT30vMF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import jax\n",
        "from jax import random, grad, jit, vmap\n",
        "from jax.config import config\n",
        "from jax.lib import xla_bridge\n",
        "import jax.numpy as np\n",
        "import neural_tangents as nt\n",
        "from neural_tangents import stax\n",
        "from jax.experimental import optimizers\n",
        "\n",
        "from livelossplot import PlotLosses\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm.notebook import tqdm as tqdm\n",
        "import matplotlib\n",
        "import matplotlib.pylab as pylab\n",
        "from matplotlib.lines import Line2D\n",
        "\n",
        "import time\n",
        "\n",
        "import numpy as onp"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnC7achEw60P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Utils\n",
        "\n",
        "fplot = lambda x : np.fft.fftshift(np.log10(np.abs(np.fft.fft(x))))\n",
        "\n",
        "# Signal makers\n",
        "\n",
        "def sample_random_signal(key, decay_vec):\n",
        "  N = decay_vec.shape[0]\n",
        "  raw = random.normal(key, [N, 2]) @ np.array([1, 1j])\n",
        "  signal_f = raw * decay_vec\n",
        "  signal = np.real(np.fft.ifft(signal_f))\n",
        "  return signal\n",
        "\n",
        "def sample_random_powerlaw(key, N, power):\n",
        "  coords = np.float32(np.fft.ifftshift(1 + N//2 - np.abs(np.fft.fftshift(np.arange(N)) - N//2)))\n",
        "  decay_vec = coords ** -power\n",
        "  return sample_random_signal(key, decay_vec) # * 100\n",
        "\n",
        "\n",
        "# Network \n",
        "\n",
        "def make_network(num_layers, num_channels, num_outputs=1):\n",
        "  layers = []\n",
        "  for i in range(num_layers-1):\n",
        "      layers.append(stax.Dense(num_channels, parameterization='standard'))\n",
        "    #   layers.append(stax.Dense(num_channels, parameterization='ntk'))\n",
        "      layers.append(stax.Relu(do_backprop=True))\n",
        "  layers.append(stax.Dense(num_outputs, parameterization='standard'))\n",
        "#   layers.append(stax.Dense(num_outputs, parameterization='ntk'))\n",
        "  return stax.serial(*layers)\n",
        "\n",
        "# Encoding \n",
        "\n",
        "def compute_ntk(x, avals, bvals, kernel_fn):\n",
        "    x1_enc = input_encoder(x, avals, bvals)\n",
        "    x2_enc = input_encoder(np.array([0.], dtype=np.float32), avals, bvals)\n",
        "    out = np.squeeze(kernel_fn(x1_enc, x2_enc, 'ntk'))\n",
        "    return out\n",
        "\n",
        "\n",
        "input_encoder = lambda x, a, b: np.concatenate([a * np.sin((2.*np.pi*x[...,None]) * b), \n",
        "                                                a * np.cos((2.*np.pi*x[...,None]) * b)], axis=-1) / np.linalg.norm(a) #* np.sqrt(a.shape[0])\n",
        "\n",
        "\n",
        "\n",
        "### complicated things\n",
        "\n",
        "def predict(kernel_fn, yf_test, pred0f_test, ab, t_final, eta=None):\n",
        "  N, M = yf_test.shape[-1]//2, 2\n",
        "  alias_tile = lambda f : f.reshape(list(f.shape[:-1])+[M,N]).mean((-2)).tile([1]*len(f.shape[:-1])+[M])\n",
        "  x_test = np.linspace(0., 1., N*M, endpoint=False)\n",
        "  H_row_test = compute_ntk(x_test,  *ab, kernel_fn)\n",
        "  H_t = np.real(np.fft.fft(H_row_test))\n",
        "  H_d_tile = alias_tile(H_t)\n",
        "  if eta is None:\n",
        "    H_d_tile_train = 1. - np.exp(-t_final * H_d_tile)\n",
        "  else:\n",
        "    H_d_tile_train = 1. - (1. - eta * H_d_tile) ** t_final\n",
        "  yf_train_tile = alias_tile(yf_test)\n",
        "  pred0f_train_tile = alias_tile(pred0f_test)\n",
        "  exp_term = H_d_tile_train * (yf_train_tile - pred0f_train_tile)\n",
        "  pred_train = (pred0f_train_tile + exp_term)[...,:N]\n",
        "  pred_test = pred0f_test + H_t / H_d_tile * exp_term\n",
        "\n",
        "  pred_test = np.real(np.fft.ifft(pred_test))[1::2]\n",
        "  pred_train = np.real(np.fft.ifft(pred_train))\n",
        "\n",
        "  return pred_test, pred_train\n",
        "\n",
        "predict = jit(predict, static_argnums=(0,))\n",
        "\n",
        "def predict_psnr(kernel_fn, y_test, pred0f_test, ab, t_final, eta=None):\n",
        "  yf_test = np.fft.fft(y_test)\n",
        "  N, M = yf_test.shape[-1]//2, 2\n",
        "  alias_tile = lambda f : f.reshape(list(f.shape[:-1])+[M,N]).mean((-2)).tile([1]*len(f.shape[:-1])+[M])\n",
        "  pred_test, pred_train = predict(kernel_fn, yf_test, pred0f_test, ab, t_final, eta)\n",
        "  calc_psnr = lambda f, g : -10*np.mean(np.log10(np.mean(np.abs(f - g)**2, -1)))\n",
        "  return calc_psnr(y_test[1::2], pred_test), calc_psnr(y_test[::2], pred_train)\n",
        "\n",
        "predict_psnr = jit(predict_psnr, static_argnums=(0,))\n",
        "\n",
        "def optimize(rand_key, network_size, y_test, y_gt, t_final, ab_init, name, kernel_lr=.01, iters=800):\n",
        "  kernel_fn = make_network(*network_size)[-1]\n",
        "\n",
        "  def spectrum_loss(params):\n",
        "    pred_test = predict(kernel_fn, np.fft.fft(y_test), np.zeros_like(y_test), (params[0], ab_init[1]), t_final)[0]\n",
        "    return .5 * np.mean(np.abs(y_test[1::2] - pred_test)**2) / np.prod(y_test.shape[-1:])\n",
        "\n",
        "  loss_grad = jit(jax.value_and_grad(spectrum_loss))\n",
        "\n",
        "  kernel_opt_init, kernel_opt_update, kernel_get_params = optimizers.adam(kernel_lr)\n",
        "  a_noise = np.abs(random.normal(rand_key, ab_init[0].shape)) * .001\n",
        "  kernel_opt_state = kernel_opt_init((ab_init[0] + a_noise, ab_init[1]))\n",
        "\n",
        "  losses = []\n",
        "  # plotlosses = PlotLosses()\n",
        "  groups = {'losses {}'.format(name): ['curr_test', 'gt_test', 'gt_train'],}\n",
        "  plotlosses = PlotLosses(groups=groups)\n",
        "  print('begin')\n",
        "  for i in range(iters):\n",
        "      ab = kernel_get_params(kernel_opt_state)\n",
        "      # ab = (ab[0], ab_init[1])\n",
        "      loss, grad = loss_grad(ab)\n",
        "      kernel_opt_state = kernel_opt_update(i, grad, kernel_opt_state)\n",
        "\n",
        "      gt_vals = predict_psnr(kernel_fn, y_gt, np.zeros_like(y_gt), ab, t_final)\n",
        "\n",
        "      # plotlosses.update({'curr_test':-10.*np.log10(2.*loss),}, current_step=i)\n",
        "      plotlosses.update({'curr_test':-10.*np.log10(2.*loss), \n",
        "                         'gt_test': gt_vals[0],\n",
        "                         'gt_train': gt_vals[1],}, current_step=i)\n",
        "      if i % 20 == 0:\n",
        "          plotlosses.send()\n",
        "\n",
        "  avals_optimized = kernel_get_params(kernel_opt_state)[0]\n",
        "  return avals_optimized\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXMEEOql0_Hn",
        "colab_type": "text"
      },
      "source": [
        "# Make fig 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWMfr1H50_jd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "N_train = 128\n",
        "data_powers = [1.0, 1.5, 2.0]\n",
        "N_test_signals = 8\n",
        "N_train_signals = 1024\n",
        "\n",
        "network_size = (4, 1024)\n",
        "\n",
        "learning_rate = 5e-3\n",
        "sgd_iters = 1000\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjOhNefO1C5z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "0a4080b3-7e9a-4989-9988-816caa526ca7"
      },
      "source": [
        "rand_key = random.PRNGKey(0)\n",
        "\n",
        "# Signal\n",
        "M = 2\n",
        "N = N_train\n",
        "x_test = np.float32(np.linspace(0,1.,N*M,endpoint=False))\n",
        "x_train = x_test[::M]\n",
        "\n",
        "\n",
        "def data_maker(rand_key, N_pts, N_signals, p):\n",
        "  rand_key, *ensemble = random.split(rand_key, 1 + N_signals)\n",
        "  data = np.stack([sample_random_powerlaw(ensemble[i], N_pts, p) for i in range(N_signals)])\n",
        "  data = (data - data.min()) / (data.max() - data.min())  - .5\n",
        "  return data, rand_key\n",
        "\n",
        "\n",
        "s_dict = {}\n",
        "for p in data_powers:\n",
        "  ret, rand_key = data_maker(rand_key, N*M, N_test_signals, p)\n",
        "  s_dict['data_p{}'.format(p)] = ret\n",
        "\n",
        "\n",
        "# Kernels\n",
        "bvals = np.float32(np.arange(1, N//2+1))\n",
        "ab_dict = {}\n",
        "powers = np.linspace(0, 2, 17)\n",
        "ab_dict.update({'power_{}'.format(p) : (bvals**-p, bvals) for p in powers})\n",
        "ab_dict['power_infty'] = (np.eye(bvals.shape[0])[0], bvals)\n",
        "\n",
        "\n",
        "# optimize\n",
        "\n",
        "ab_opt_dict = {}\n",
        "t_final = learning_rate * sgd_iters\n",
        "\n",
        "for p in data_powers:\n",
        "  k = 'opt_fam_p{}'.format(p)\n",
        "  ab = ab_dict['power_infty']\n",
        "  train_signals, rand_key = data_maker(rand_key, N*M, N_train_signals, p)\n",
        "  rand_key, opt_key = random.split(rand_key)\n",
        "  a_opt = optimize(opt_key, network_size, train_signals, s_dict['data_p{}'.format(p)][0], t_final, ab, k)\n",
        "\n",
        "  ab_opt_dict[k] = (a_opt, bvals)\n",
        "\n",
        "ab_dict.update(ab_opt_dict)\n",
        "\n",
        "for k in ab_opt_dict:\n",
        "  plt.plot(np.abs(ab_opt_dict[k][0]), label=k)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "losses opt_fam_p2.0\n",
            "\tcurr_test        \t (min:   76.712, max:   93.227, cur:   93.028)\n",
            "\tgt_test          \t (min:   41.593, max:   56.225, cur:   56.073)\n",
            "\tgt_train         \t (min:   41.734, max:   66.044, cur:   65.736)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Riu56rX23G5r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "18894094bd134884bae392bb4d235c27",
            "e296a0cc3a9a4fcab670df25cee0163e",
            "314125cdacf6492893351ddd546ba63e",
            "505ce12651334a1498b2945c0b8d14ed",
            "7a70fe0a91894427a8a9955a0ea101f8",
            "c2d25417e3f9499c99a620d3ec66242c",
            "14b8912780f74bc8b4fa7e044fdc85b6",
            "38d727f2064d4ef7847f037a5d64afb0",
            "9bc6393aa4f94e848fe4b5ed9530e7fc",
            "e0b052277e52490cb07c35416a740a50",
            "428a54dcacfe4decba05f3c352a6a1e3",
            "90741a6889d04ad9b19f8fdd8c161303",
            "cfc8c30eaace4877bac6a4d43dffeb78",
            "8818e22646354893904eefb1660a2cf4",
            "fbe8a460ef2d473f97c2ca51a642b132",
            "2509995f532644c7bc2e10dabfef827f",
            "1e47952a64974d19a4ca73b5537969e4",
            "0767916c7c2d4b83a4a3bee50b65505d",
            "a6bcf620a53c42818b2974d876e34c07",
            "ed5f1c43668b45ed8715ced85bb0a866",
            "347f925efa0144c8a52d1ac6c29947fa",
            "baa8d0fe62604437b839ed2ab5b00c54",
            "2f50e77c624b464bac9d7e4db6ea5b09",
            "1b2680b63ec64778a6487e3b7e576ac5",
            "24a9750cdd02426fac2928d938121a75",
            "7132b4bfcfbe46f49fa797cf315e32b5",
            "11acee87a28a4cc78fc52f7352c3df74",
            "da5036bd2b65477a85ab3f5649439299",
            "fb5680ac3da44753861aa061efd488a5",
            "a470fe17aaba42fd850f825026114d31",
            "52ccae2863a04377917d097cc84a4b01",
            "03e8674388984ad19bc7eabb603ca575"
          ]
        },
        "outputId": "316b2245-0478-4f20-ff0c-f406b04e176d"
      },
      "source": [
        "def train_model_lite(rand_key, network_size, lr, iters, \n",
        "                train_input, test_input, optimizer, ab):\n",
        "    init_fn, apply_fn, kernel_fn = make_network(*network_size)\n",
        "\n",
        "    run_model = jit(lambda params, ab, x: np.squeeze(apply_fn(params, input_encoder(x, *ab))))\n",
        "    model_loss = jit(lambda params, ab, x, y: .5 * np.sum((run_model(params, ab, x) - y) ** 2))\n",
        "    model_psnr = jit(lambda params, ab, x, y: -10 * np.log10(np.mean((run_model(params, ab, x) - y) ** 2)))\n",
        "    model_grad_loss = jit(lambda params, ab, x, y: jax.grad(model_loss)(params, ab, x, y))\n",
        "\n",
        "    opt_init, opt_update, get_params = optimizer(lr)\n",
        "    opt_update = jit(opt_update)\n",
        "\n",
        "    _, params = init_fn(rand_key, (-1, input_encoder(train_input[0], *ab).shape[-1]))\n",
        "    opt_state = opt_init(params)\n",
        "\n",
        "    pred0 = run_model(get_params(opt_state), ab, test_input[0])\n",
        "    \n",
        "    for i in (range(iters)):\n",
        "        opt_state = opt_update(i, model_grad_loss(get_params(opt_state), ab, *train_input), opt_state)\n",
        "    \n",
        "    train_psnr = model_psnr(get_params(opt_state), ab, *train_input)\n",
        "    test_psnr = model_psnr(get_params(opt_state), ab, test_input[0][1::2], test_input[1][1::2])\n",
        "    theory = predict_psnr(kernel_fn, test_input[1], pred0, ab, i * lr)\n",
        "\n",
        "    return get_params(opt_state), train_psnr, test_psnr, theory\n",
        "\n",
        "\n",
        "print(ab_dict.keys())\n",
        "\n",
        "outputs = {}\n",
        "for d_key in tqdm(s_dict):\n",
        "  s_list = s_dict[d_key]\n",
        "  outputs[d_key] = {}\n",
        "  for k in tqdm(ab_dict, leave=False):\n",
        "    outputs[d_key][k] = []\n",
        "\n",
        "    rand_key, key = random.split(rand_key)\n",
        "    train_fn = lambda s, key : train_model_lite(key, network_size, learning_rate, sgd_iters,\n",
        "                        (x_train, s[::2]), (x_test, s), optimizers.sgd, ab_dict[k])\n",
        "    \n",
        "    ensemble_key = random.split(key, s_list.shape[0])\n",
        "    z = vmap(train_fn, in_axes=(0, 0))(s_list, ensemble_key)\n",
        "    outputs[d_key][k] = z\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['power_0.0', 'power_0.125', 'power_0.25', 'power_0.375', 'power_0.5', 'power_0.625', 'power_0.75', 'power_0.875', 'power_1.0', 'power_1.125', 'power_1.25', 'power_1.375', 'power_1.5', 'power_1.625', 'power_1.75', 'power_1.875', 'power_2.0', 'power_infty', 'opt_fam_p1.0', 'opt_fam_p1.5', 'opt_fam_p2.0'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "18894094bd134884bae392bb4d235c27"
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=21.0), HTML(value='')))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9bc6393aa4f94e848fe4b5ed9530e7fc"
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/home/jupyter-tancik/.conda/envs/jax-tf/lib/python3.7/site-packages/jax/lax/lax.py:5222: UserWarning: Explicitly requested dtype <class 'jax.numpy.lax_numpy.float64'> requested in astype is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n",
            "  warnings.warn(msg.format(dtype, fun_name , truncated_dtype))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=21.0), HTML(value='')))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e47952a64974d19a4ca73b5537969e4"
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=21.0), HTML(value='')))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24a9750cdd02426fac2928d938121a75"
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kg8_PVizvr8G",
        "colab_type": "text"
      },
      "source": [
        "#Figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVXVRH6F7gw5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "b46f16d0-2347-4ff0-b318-8c1207a4c2bf"
      },
      "source": [
        "H_rows = {k : compute_ntk(x_train, *ab_dict[k], make_network(*(network_size))[-1]) for k in ab_dict if 'opt' in k}\n",
        "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
        "colors = prop_cycle.by_key()['color']\n",
        "\n",
        "params = {'legend.fontsize': 22,\n",
        "         'axes.labelsize': 24,\n",
        "         'axes.titlesize': 28,\n",
        "         'xtick.labelsize':22,\n",
        "         'ytick.labelsize':22}\n",
        "pylab.rcParams.update(params)\n",
        "\n",
        "\n",
        "matplotlib.rcParams['mathtext.fontset'] = 'cm'\n",
        "matplotlib.rcParams['mathtext.rm'] = 'serif'\n",
        "\n",
        "plt.rcParams[\"font.family\"] = \"cmr10\"\n",
        "\n",
        "\n",
        "N_fams = len(data_powers)\n",
        "\n",
        "\n",
        "fig3 = plt.figure(constrained_layout=True, figsize=(20,6))\n",
        "gs = fig3.add_gridspec(1,2)\n",
        "\n",
        "colors_k = np.array([[0.8872, 0.4281, 0.1875],\n",
        "    # [0.8136, 0.6844, 0.0696],\n",
        "    [0.2634, 0.6634, 0.4134],\n",
        "    [0.0943, 0.5937, 0.8793],\n",
        "    [0.3936, 0.2946, 0.6330],\n",
        "    [0.7123, 0.2705, 0.3795]])\n",
        "\n",
        "linewidth = 4\n",
        "legend_offset = -.27\n",
        "\n",
        "ax = fig3.add_subplot(gs[0,0])\n",
        "fams = ['1.0', '1.5', '2.0']\n",
        "power_infinity = compute_ntk(x_train, *ab_dict['power_infty'], make_network(*(network_size))[-1])\n",
        "plt.semilogy(10**fplot(power_infinity), color='k', label=fr'Power $\\infty$', linewidth=linewidth, linestyle='-', alpha=.6)\n",
        "for i, k in enumerate(H_rows):\n",
        "  plt.semilogy(10**fplot(H_rows[k]), color=colors_k[i], label=fr'Opt. for $\\alpha={fams[i]}$', linewidth=linewidth, alpha=.8)\n",
        "ax.set_title('(a) NTK Fourier spectrum', y=legend_offset)\n",
        "plt.xticks([0,32,64,96,128], ['$-\\pi$','$-\\pi/2$','$0$','$\\pi/2$','$\\pi$'])\n",
        "\n",
        "ax.legend(loc='upper right', handlelength=1)\n",
        "plt.xlabel('Frequency')\n",
        "plt.ylabel(r'Magnitude')\n",
        "plt.grid(True, which='major', alpha=.3)\n",
        "\n",
        "for i, d in enumerate(s_dict):\n",
        "  ax = fig3.add_subplot(gs[0,1])\n",
        "  \n",
        "  names = list(outputs[d].keys())\n",
        "\n",
        "  names = names[:-N_fams] + [names[-N_fams+i]]\n",
        "\n",
        "  xvals = np.arange(len(names))\n",
        "  values_raw = np.array([10**(outputs[d][n][2]/-10) for n in names])\n",
        "\n",
        "  values = np.mean(values_raw, axis=-1)[:-1]\n",
        "  values_opt = np.mean(values_raw, axis=-1)[-1]\n",
        "  values_std = np.std(values_raw, axis=-1)[:-1]\n",
        "  values_std_opt = np.std(values_raw, axis=-1)[-1]\n",
        "\n",
        "  x_axis = np.linspace(0,2,values.shape[0]-1)\n",
        "  x_axis = np.append(x_axis, 2.5)\n",
        "  best_x = np.argmin(values)\n",
        "  plt.scatter([x_axis[best_x]], [values[best_x]], c=colors_k[i]*.75, s=200, marker='x', )\n",
        "  ax.hlines(values_opt, xmin=x_axis[best_x]-.1, xmax=x_axis[best_x]+.1, color=colors_k[i]*.75, linestyle=':', linewidth=linewidth, alpha=.8)\n",
        "\n",
        "  plt.semilogy(x_axis, values, label='Trained', c=colors_k[i], linewidth=linewidth, alpha=.3, solid_capstyle='butt')\n",
        "  plt.fill_between(x_axis, values-values_std, values+values_std, color=colors_k[i], alpha=.15)\n",
        "  plt.grid(True, which='major', alpha=.3)\n",
        "\n",
        "  if i==0:\n",
        "    ax.set_ylabel('Mean squared error')\n",
        "\n",
        "\n",
        "plt.text(.85, .93, r'$\\alpha=1.0$', transform=ax.transAxes,  fontsize=20)\n",
        "plt.text(.85, .62, r'$\\alpha=1.5$', transform=ax.transAxes,  fontsize=20)\n",
        "plt.text(.74, .42, r'Signal $\\alpha=2.0$', transform=ax.transAxes,  fontsize=20)\n",
        "plt.xlabel('Fourier features power distribution')\n",
        "xticks = [0,.5,1,1.5,2,2.5]\n",
        "ax.set_xticks(xticks)\n",
        "ax.set_xticklabels([\"$0.0$\",\"$0.5$\",\"$1.0$\",\"$1.5$\",\"$2.0$\", \"$\\infty$\"])\n",
        "ax.tick_params(axis='x')\n",
        "\n",
        "d=.01\n",
        "kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)\n",
        "ax.plot((.86-d, .86+d), (-d*2, d*2), **kwargs)\n",
        "ax.plot((.87-d, .87+d), (-d*2, d*2), **kwargs)\n",
        "\n",
        "ax.set_title('(b) Fourier features mapping performances', y=legend_offset)\n",
        "\n",
        "custom_lines = [Line2D([], [], color='gray', linestyle='-'),\n",
        "                Line2D([], [], marker='x', color='k', markersize=10, linestyle='None'),\n",
        "                Line2D([], [], color='k', markersize=10, linestyle=':', linewidth=linewidth)]\n",
        "\n",
        "ax.legend(custom_lines, ['Power', 'Best Power',r'Opt. for $\\alpha$'], loc='center left', bbox_to_anchor=(1,.5),  ncol=1, framealpha=.95, handlelength=1.6)\n",
        "# ax.legend(custom_lines, [names[0], names[1], names[2], names[3], 'Test', 'Train'], loc='center left', bbox_to_anchor=(1,.5),  ncol=1, framealpha=.95, handlelength=1.6)\n",
        "\n",
        "plt.savefig('supp_opt.pdf', bbox_inches='tight', pad_inches=0)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:matplotlib.axes._axes:'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "/home/jupyter-tancik/.conda/envs/jax-tf/lib/python3.7/site-packages/ipykernel_launcher.py:50: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "WARNING:matplotlib.axes._axes:'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
            "/home/jupyter-tancik/.conda/envs/jax-tf/lib/python3.7/site-packages/ipykernel_launcher.py:50: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "WARNING:matplotlib.axes._axes:'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2dvtenRqItG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}
